[batou069]

AFAIK llamaindex is built on langchain to save you a lot of time and lines of code, and also constantly adding new stuff (as does langchain) as dataloaders, different indeces, all kinds of cool stuff like nodes pre and post processing, reranking, query optimization, query decomposition, custom query engines and whatnot. you can still use langchain, for example when using agents or more realistically when going the route of textsplitting by token size. I'd say its complementary, do whatever you can do with llama_index and whatever you cant use langchain.

[towelpluswater]

after using both of them for awhile, i see them as complementary as well. i'm more likely to use langchain for execution of tasks, and llamaindex for backend optimization and building out 'embeddings data pipelines'. if i need something from langchain in llamaindex, i can bring it in, and vice versa.

the more folks experiment with this stuff, i think they'll see where it all comes together, and where some of the crossover is. but given how quickly everything changes in this space, i'm glad there's a clear focus from each team on their core strengths rather than throwing the kitchen sink of new papers at it.